44 citations · 70 across the 5 of their papers we have counts for
5 papers
PIE: Performance Interval Estimation for Free-Form Generation Tasks
Chi-Yang Hsu, Alexander Braylan, Yiheng Su +2
Confidence estimation infers a probability for whether each model output is correct or not. While predicting such binary correctness is sensible for tasks with exact answers, free-…
A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation Tasks
Alexander Braylan, Madalyn Marabella, Omar Alonso +1
Human annotations are vital to supervised learning, yet annotators often disagree on the correct label, especially as annotation tasks increase in complexity. A strategy to improve…
Measuring Annotator Agreement Generally across Complex Structured, Multi-object, and Free-text Annotation Tasks
Alexander Braylan, Omar Alonso, Matthew Lease
When annotators label data, a key metric for quality assurance is inter-annotator agreement (IAA): the extent to which annotators agree on their labels. Though many IAA measures ex…
Neural Information Retrieval: A Literature Review
Ye Zhang, Md Mustafizur Rahman, Alex Braylan +12
A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and…
Reuse of Neural Modules for General Video Game Playing
Alexander Braylan, Mark Hollenbeck, Elliot Meyerson +1
A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Network…